Overview

Dataset statistics

Number of variables35
Number of observations4302
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory318.7 B

Variable types

Numeric15
Categorical17
Boolean3

Alerts

EmployeeCount has constant value "1.0"Constant
Over18 has constant value "True"Constant
StandardHours has constant value "80.0"Constant
MonthlyIncome is highly overall correlated with TotalWorkingYears and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
TotalWorkingYears is highly overall correlated with MonthlyIncomeHigh correlation
YearsAtCompany is highly overall correlated with YearsInCurrentRole and 1 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 1 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
EmployeeNumber has unique valuesUnique
NumCompaniesWorked has 569 (13.2%) zerosZeros
TrainingTimesLastYear has 161 (3.7%) zerosZeros
YearsAtCompany has 126 (2.9%) zerosZeros
YearsInCurrentRole has 646 (15.0%) zerosZeros
YearsSinceLastPromotion has 1580 (36.7%) zerosZeros
YearsWithCurrManager has 716 (16.6%) zerosZeros

Reproduction

Analysis started2023-07-15 16:08:30.989881
Analysis finished2023-07-15 16:09:58.937950
Duration1 minute and 27.95 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

EmployeeNumber
Real number (ℝ)

Distinct4302
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4809.2431
Minimum1
Maximum7979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:09:59.102525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile333.05
Q11680.25
median5828.5
Q36903.75
95-th percentile7763.95
Maximum7979
Range7978
Interquartile range (IQR)5223.5

Descriptive statistics

Standard deviation2643.0402
Coefficient of variation (CV)0.54957508
Kurtosis-1.2047691
Mean4809.2431
Median Absolute Deviation (MAD)1323
Skewness-0.653578
Sum20689364
Variance6985661.5
MonotonicityNot monotonic
2023-07-15T16:09:59.435082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 1
 
< 0.1%
6551 1
 
< 0.1%
6537 1
 
< 0.1%
6538 1
 
< 0.1%
6539 1
 
< 0.1%
6540 1
 
< 0.1%
6541 1
 
< 0.1%
6542 1
 
< 0.1%
6543 1
 
< 0.1%
6544 1
 
< 0.1%
Other values (4292) 4292
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
7979 1
< 0.1%
7978 1
< 0.1%
7977 1
< 0.1%
7976 1
< 0.1%
7975 1
< 0.1%
7974 1
< 0.1%
7973 1
< 0.1%
7972 1
< 0.1%
7971 1
< 0.1%
7970 1
< 0.1%

Age
Real number (ℝ)

Distinct179
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.1232
Minimum18
Maximum9890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:09:59.758028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q131
median36
Q345
95-th percentile3627.9
Maximum9890
Range9872
Interquartile range (IQR)14

Descriptive statistics

Standard deviation1591.1534
Coefficient of variation (CV)3.7079175
Kurtosis18.193393
Mean429.1232
Median Absolute Deviation (MAD)7
Skewness4.3194914
Sum1846088
Variance2531769.2
MonotonicityNot monotonic
2023-07-15T16:10:00.047604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 218
 
5.1%
35 212
 
4.9%
29 190
 
4.4%
36 185
 
4.3%
31 182
 
4.2%
38 171
 
4.0%
40 164
 
3.8%
33 161
 
3.7%
32 161
 
3.7%
30 159
 
3.7%
Other values (169) 2499
58.1%
ValueCountFrequency (%)
18 19
 
0.4%
19 23
 
0.5%
20 32
 
0.7%
21 42
 
1.0%
22 53
 
1.2%
23 37
 
0.9%
24 69
1.6%
25 73
1.7%
26 106
2.5%
27 135
3.1%
ValueCountFrequency (%)
9890 2
< 0.1%
9866 3
0.1%
9661 3
0.1%
9612 3
0.1%
9581 3
0.1%
9531 3
0.1%
9529 2
< 0.1%
9384 2
< 0.1%
9330 3
0.1%
9243 2
< 0.1%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Travel_Rarely
3041 
Travel_Frequently
816 
Non-Travel
445 

Length

Max length17
Median length13
Mean length13.448396
Min length10

Characters and Unicode

Total characters57855
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 3041
70.7%
Travel_Frequently 816
 
19.0%
Non-Travel 445
 
10.3%

Length

2023-07-15T16:10:00.339826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:00.622761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 3041
70.7%
travel_frequently 816
 
19.0%
non-travel 445
 
10.3%

Most occurring characters

ValueCountFrequency (%)
e 8975
15.5%
r 8159
14.1%
l 8159
14.1%
a 7343
12.7%
T 4302
7.4%
v 4302
7.4%
y 3857
6.7%
_ 3857
6.7%
R 3041
 
5.3%
n 1261
 
2.2%
Other values (7) 4599
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44949
77.7%
Uppercase Letter 8604
 
14.9%
Connector Punctuation 3857
 
6.7%
Dash Punctuation 445
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8975
20.0%
r 8159
18.2%
l 8159
18.2%
a 7343
16.3%
v 4302
9.6%
y 3857
8.6%
n 1261
 
2.8%
q 816
 
1.8%
u 816
 
1.8%
t 816
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 4302
50.0%
R 3041
35.3%
F 816
 
9.5%
N 445
 
5.2%
Connector Punctuation
ValueCountFrequency (%)
_ 3857
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 445
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53553
92.6%
Common 4302
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8975
16.8%
r 8159
15.2%
l 8159
15.2%
a 7343
13.7%
T 4302
8.0%
v 4302
8.0%
y 3857
7.2%
R 3041
 
5.7%
n 1261
 
2.4%
F 816
 
1.5%
Other values (5) 3338
 
6.2%
Common
ValueCountFrequency (%)
_ 3857
89.7%
- 445
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8975
15.5%
r 8159
14.1%
l 8159
14.1%
a 7343
12.7%
T 4302
7.4%
v 4302
7.4%
y 3857
6.7%
_ 3857
6.7%
R 3041
 
5.3%
n 1261
 
2.2%
Other values (7) 4599
7.9%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean800.84379
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:00.869704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile167.05
Q1457
median804
Q31162
95-th percentile1427
Maximum1499
Range1397
Interquartile range (IQR)705

Descriptive statistics

Standard deviation405.65576
Coefficient of variation (CV)0.50653544
Kurtosis-1.228175
Mean800.84379
Median Absolute Deviation (MAD)354
Skewness-0.00064347544
Sum3445230
Variance164556.6
MonotonicityNot monotonic
2023-07-15T16:10:01.149371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 16
 
0.4%
1146 16
 
0.4%
530 16
 
0.4%
1329 16
 
0.4%
329 16
 
0.4%
430 15
 
0.3%
1283 15
 
0.3%
1082 15
 
0.3%
350 14
 
0.3%
1490 13
 
0.3%
Other values (876) 4150
96.5%
ValueCountFrequency (%)
102 2
 
< 0.1%
103 4
0.1%
104 3
0.1%
105 2
 
< 0.1%
106 2
 
< 0.1%
107 2
 
< 0.1%
109 4
0.1%
111 7
0.2%
115 3
0.1%
116 7
0.2%
ValueCountFrequency (%)
1499 2
 
< 0.1%
1498 3
 
0.1%
1496 6
0.1%
1495 10
0.2%
1492 3
 
0.1%
1490 13
0.3%
1488 3
 
0.1%
1485 9
0.2%
1482 3
 
0.1%
1480 7
0.2%

Department
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Research & Development
2815 
Sales
1305 
Human Resources
 
182

Length

Max length22
Median length22
Mean length16.546955
Min length5

Characters and Unicode

Total characters71185
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 2815
65.4%
Sales 1305
30.3%
Human Resources 182
 
4.2%

Length

2023-07-15T16:10:01.485843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:01.810776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
research 2815
27.8%
2815
27.8%
development 2815
27.8%
sales 1305
12.9%
human 182
 
1.8%
resources 182
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 15744
22.1%
5812
 
8.2%
s 4484
 
6.3%
a 4302
 
6.0%
l 4120
 
5.8%
R 2997
 
4.2%
r 2997
 
4.2%
c 2997
 
4.2%
n 2997
 
4.2%
m 2997
 
4.2%
Other values (10) 21738
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55259
77.6%
Uppercase Letter 7299
 
10.3%
Space Separator 5812
 
8.2%
Other Punctuation 2815
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15744
28.5%
s 4484
 
8.1%
a 4302
 
7.8%
l 4120
 
7.5%
r 2997
 
5.4%
c 2997
 
5.4%
n 2997
 
5.4%
m 2997
 
5.4%
o 2997
 
5.4%
p 2815
 
5.1%
Other values (4) 8809
15.9%
Uppercase Letter
ValueCountFrequency (%)
R 2997
41.1%
D 2815
38.6%
S 1305
17.9%
H 182
 
2.5%
Space Separator
ValueCountFrequency (%)
5812
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62558
87.9%
Common 8627
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15744
25.2%
s 4484
 
7.2%
a 4302
 
6.9%
l 4120
 
6.6%
R 2997
 
4.8%
r 2997
 
4.8%
c 2997
 
4.8%
n 2997
 
4.8%
m 2997
 
4.8%
o 2997
 
4.8%
Other values (8) 15926
25.5%
Common
ValueCountFrequency (%)
5812
67.4%
& 2815
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15744
22.1%
5812
 
8.2%
s 4484
 
6.3%
a 4302
 
6.0%
l 4120
 
5.8%
R 2997
 
4.2%
r 2997
 
4.2%
c 2997
 
4.2%
n 2997
 
4.2%
m 2997
 
4.2%
Other values (10) 21738
30.5%

DistanceFromHome
Real number (ℝ)

Distinct158
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34478.313
Minimum1
Maximum999590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:02.087637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q317
95-th percentile273998.2
Maximum999590
Range999589
Interquartile range (IQR)15

Descriptive statistics

Standard deviation147682.32
Coefficient of variation (CV)4.2833393
Kurtosis21.861552
Mean34478.313
Median Absolute Deviation (MAD)6
Skewness4.6759471
Sum1.483257 × 108
Variance2.1810066 × 1010
MonotonicityNot monotonic
2023-07-15T16:10:02.396120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 589
 
13.7%
1 568
 
13.2%
10 239
 
5.6%
3 227
 
5.3%
7 225
 
5.2%
9 224
 
5.2%
8 220
 
5.1%
5 175
 
4.1%
4 163
 
3.8%
6 155
 
3.6%
Other values (148) 1517
35.3%
ValueCountFrequency (%)
1 568
13.2%
2 589
13.7%
3 227
 
5.3%
4 163
 
3.8%
5 175
 
4.1%
6 155
 
3.6%
7 225
 
5.2%
8 220
 
5.1%
9 224
 
5.2%
10 239
5.6%
ValueCountFrequency (%)
999590 3
0.1%
997422 2
< 0.1%
993161 3
0.1%
992947 2
< 0.1%
975446 3
0.1%
971787 2
< 0.1%
968203 2
< 0.1%
963929 1
 
< 0.1%
946786 3
0.1%
934448 3
0.1%

Education
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
3.0
1674 
4.0
1155 
2.0
826 
1.0
506 
5.0
 
141

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 1674
38.9%
4.0 1155
26.8%
2.0 826
19.2%
1.0 506
 
11.8%
5.0 141
 
3.3%

Length

2023-07-15T16:10:02.684705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:02.968828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1674
38.9%
4.0 1155
26.8%
2.0 826
19.2%
1.0 506
 
11.8%
5.0 141
 
3.3%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1674
 
13.0%
4 1155
 
8.9%
2 826
 
6.4%
1 506
 
3.9%
5 141
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
3 1674
 
19.5%
4 1155
 
13.4%
2 826
 
9.6%
1 506
 
5.9%
5 141
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1674
 
13.0%
4 1155
 
8.9%
2 826
 
6.4%
1 506
 
3.9%
5 141
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1674
 
13.0%
4 1155
 
8.9%
2 826
 
6.4%
1 506
 
3.9%
5 141
 
1.1%

EducationField
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Life Sciences
1768 
Medical
1344 
Marketing
472 
Technical Degree
393 
Other
245 

Length

Max length16
Median length15
Mean length10.542306
Min length5

Characters and Unicode

Total characters45353
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowMedical
3rd rowOther
4th rowLife Sciences
5th rowOther

Common Values

ValueCountFrequency (%)
Life Sciences 1768
41.1%
Medical 1344
31.2%
Marketing 472
 
11.0%
Technical Degree 393
 
9.1%
Other 245
 
5.7%
Human Resources 80
 
1.9%

Length

2023-07-15T16:10:03.228991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:03.574954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
life 1768
27.0%
sciences 1768
27.0%
medical 1344
20.5%
marketing 472
 
7.2%
technical 393
 
6.0%
degree 393
 
6.0%
other 245
 
3.7%
human 80
 
1.2%
resources 80
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 9097
20.1%
c 5746
12.7%
i 5745
12.7%
n 2713
 
6.0%
a 2289
 
5.0%
2241
 
4.9%
s 1928
 
4.3%
M 1816
 
4.0%
L 1768
 
3.9%
f 1768
 
3.9%
Other values (16) 10242
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36569
80.6%
Uppercase Letter 6543
 
14.4%
Space Separator 2241
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9097
24.9%
c 5746
15.7%
i 5745
15.7%
n 2713
 
7.4%
a 2289
 
6.3%
s 1928
 
5.3%
f 1768
 
4.8%
l 1737
 
4.7%
d 1344
 
3.7%
r 1190
 
3.3%
Other values (7) 3012
 
8.2%
Uppercase Letter
ValueCountFrequency (%)
M 1816
27.8%
L 1768
27.0%
S 1768
27.0%
T 393
 
6.0%
D 393
 
6.0%
O 245
 
3.7%
H 80
 
1.2%
R 80
 
1.2%
Space Separator
ValueCountFrequency (%)
2241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43112
95.1%
Common 2241
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9097
21.1%
c 5746
13.3%
i 5745
13.3%
n 2713
 
6.3%
a 2289
 
5.3%
s 1928
 
4.5%
M 1816
 
4.2%
L 1768
 
4.1%
f 1768
 
4.1%
S 1768
 
4.1%
Other values (15) 8474
19.7%
Common
ValueCountFrequency (%)
2241
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9097
20.1%
c 5746
12.7%
i 5745
12.7%
n 2713
 
6.0%
a 2289
 
5.0%
2241
 
4.9%
s 1928
 
4.3%
M 1816
 
4.0%
L 1768
 
3.9%
f 1768
 
3.9%
Other values (16) 10242
22.6%

EmployeeCount
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
1.0
4302 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4302
100.0%

Length

2023-07-15T16:10:03.872417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:04.144636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4302
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4302
33.3%
. 4302
33.3%
0 4302
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4302
50.0%
0 4302
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4302
33.3%
. 4302
33.3%
0 4302
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4302
33.3%
. 4302
33.3%
0 4302
33.3%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
3.0
1358 
4.0
1290 
1.0
842 
2.0
812 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 1358
31.6%
4.0 1290
30.0%
1.0 842
19.6%
2.0 812
18.9%

Length

2023-07-15T16:10:04.463573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:04.933664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1358
31.6%
4.0 1290
30.0%
1.0 842
19.6%
2.0 812
18.9%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1358
 
10.5%
4 1290
 
10.0%
1 842
 
6.5%
2 812
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
3 1358
 
15.8%
4 1290
 
15.0%
1 842
 
9.8%
2 812
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1358
 
10.5%
4 1290
 
10.0%
1 842
 
6.5%
2 812
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1358
 
10.5%
4 1290
 
10.0%
1 842
 
6.5%
2 812
 
6.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Male
2595 
Female
1707 

Length

Max length6
Median length4
Mean length4.7935844
Min length4

Characters and Unicode

Total characters20622
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 2595
60.3%
Female 1707
39.7%

Length

2023-07-15T16:10:05.396793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:07.251537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 2595
60.3%
female 1707
39.7%

Most occurring characters

ValueCountFrequency (%)
e 6009
29.1%
a 4302
20.9%
l 4302
20.9%
M 2595
12.6%
F 1707
 
8.3%
m 1707
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16320
79.1%
Uppercase Letter 4302
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6009
36.8%
a 4302
26.4%
l 4302
26.4%
m 1707
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M 2595
60.3%
F 1707
39.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 20622
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6009
29.1%
a 4302
20.9%
l 4302
20.9%
M 2595
12.6%
F 1707
 
8.3%
m 1707
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6009
29.1%
a 4302
20.9%
l 4302
20.9%
M 2595
12.6%
F 1707
 
8.3%
m 1707
 
8.3%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.036495
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:07.510968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.298352
Coefficient of variation (CV)0.30738082
Kurtosis-1.1906122
Mean66.036495
Median Absolute Deviation (MAD)18
Skewness-0.029402922
Sum284089
Variance412.02308
MonotonicityNot monotonic
2023-07-15T16:10:07.821703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 86
 
2.0%
66 83
 
1.9%
48 82
 
1.9%
57 81
 
1.9%
79 81
 
1.9%
52 81
 
1.9%
96 79
 
1.8%
84 79
 
1.8%
56 79
 
1.8%
54 77
 
1.8%
Other values (61) 3494
81.2%
ValueCountFrequency (%)
30 55
1.3%
31 43
1.0%
32 70
1.6%
33 54
1.3%
34 33
0.8%
35 43
1.0%
36 55
1.3%
37 50
1.2%
38 39
0.9%
39 52
1.2%
ValueCountFrequency (%)
100 59
1.4%
99 58
1.3%
98 86
2.0%
97 62
1.4%
96 79
1.8%
95 70
1.6%
94 69
1.6%
93 48
1.1%
92 74
1.7%
91 49
1.1%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
3.0
2540 
2.0
1086 
4.0
442 
1.0
 
234

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 2540
59.0%
2.0 1086
25.2%
4.0 442
 
10.3%
1.0 234
 
5.4%

Length

2023-07-15T16:10:08.108236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:08.371134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 2540
59.0%
2.0 1086
25.2%
4.0 442
 
10.3%
1.0 234
 
5.4%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 2540
19.7%
2 1086
 
8.4%
4 442
 
3.4%
1 234
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
3 2540
29.5%
2 1086
 
12.6%
4 442
 
5.1%
1 234
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 2540
19.7%
2 1086
 
8.4%
4 442
 
3.4%
1 234
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 2540
19.7%
2 1086
 
8.4%
4 442
 
3.4%
1 234
 
1.8%

JobLevel
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
1.0
1616 
2.0
1548 
3.0
631 
4.0
314 
5.0
193 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1616
37.6%
2.0 1548
36.0%
3.0 631
 
14.7%
4.0 314
 
7.3%
5.0 193
 
4.5%

Length

2023-07-15T16:10:08.652513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:08.959981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1616
37.6%
2.0 1548
36.0%
3.0 631
 
14.7%
4.0 314
 
7.3%
5.0 193
 
4.5%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
1 1616
 
12.5%
2 1548
 
12.0%
3 631
 
4.9%
4 314
 
2.4%
5 193
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
1 1616
 
18.8%
2 1548
 
18.0%
3 631
 
7.3%
4 314
 
3.6%
5 193
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
1 1616
 
12.5%
2 1548
 
12.0%
3 631
 
4.9%
4 314
 
2.4%
5 193
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
1 1616
 
12.5%
2 1548
 
12.0%
3 631
 
4.9%
4 314
 
2.4%
5 193
 
1.5%

JobRole
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Sales Executive
950 
Research Scientist
883 
Laboratory Technician
755 
Manufacturing Director
417 
Healthcare Representative
384 
Other values (4)
913 

Length

Max length25
Median length21
Mean length18.099721
Min length7

Characters and Unicode

Total characters77865
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowManufacturing Director
3rd rowResearch Scientist
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive 950
22.1%
Research Scientist 883
20.5%
Laboratory Technician 755
17.5%
Manufacturing Director 417
9.7%
Healthcare Representative 384
8.9%
Manager 287
 
6.7%
Sales Representative 247
 
5.7%
Research Director 229
 
5.3%
Human Resources 150
 
3.5%

Length

2023-07-15T16:10:09.225643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:09.557667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sales 1197
14.4%
research 1112
13.4%
executive 950
11.4%
scientist 883
10.6%
laboratory 755
9.1%
technician 755
9.1%
director 646
7.8%
representative 631
7.6%
manufacturing 417
 
5.0%
healthcare 384
 
4.6%
Other values (3) 587
7.1%

Most occurring characters

ValueCountFrequency (%)
e 11484
14.7%
a 7531
 
9.7%
t 6180
 
7.9%
c 6052
 
7.8%
i 5920
 
7.6%
r 5783
 
7.4%
n 4295
 
5.5%
s 4123
 
5.3%
4015
 
5.2%
o 2306
 
3.0%
Other values (19) 20176
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65533
84.2%
Uppercase Letter 8317
 
10.7%
Space Separator 4015
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11484
17.5%
a 7531
11.5%
t 6180
9.4%
c 6052
9.2%
i 5920
9.0%
r 5783
8.8%
n 4295
 
6.6%
s 4123
 
6.3%
o 2306
 
3.5%
h 2251
 
3.4%
Other values (10) 9608
14.7%
Uppercase Letter
ValueCountFrequency (%)
S 2080
25.0%
R 1893
22.8%
E 950
11.4%
L 755
 
9.1%
T 755
 
9.1%
M 704
 
8.5%
D 646
 
7.8%
H 534
 
6.4%
Space Separator
ValueCountFrequency (%)
4015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73850
94.8%
Common 4015
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11484
15.6%
a 7531
10.2%
t 6180
 
8.4%
c 6052
 
8.2%
i 5920
 
8.0%
r 5783
 
7.8%
n 4295
 
5.8%
s 4123
 
5.6%
o 2306
 
3.1%
h 2251
 
3.0%
Other values (18) 17925
24.3%
Common
ValueCountFrequency (%)
4015
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11484
14.7%
a 7531
 
9.7%
t 6180
 
7.9%
c 6052
 
7.8%
i 5920
 
7.6%
r 5783
 
7.4%
n 4295
 
5.5%
s 4123
 
5.3%
4015
 
5.2%
o 2306
 
3.0%
Other values (19) 20176
25.9%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
4.0
1344 
3.0
1281 
1.0
859 
2.0
818 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 1344
31.2%
3.0 1281
29.8%
1.0 859
20.0%
2.0 818
19.0%

Length

2023-07-15T16:10:09.866013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:10.129121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1344
31.2%
3.0 1281
29.8%
1.0 859
20.0%
2.0 818
19.0%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
4 1344
 
10.4%
3 1281
 
9.9%
1 859
 
6.7%
2 818
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
4 1344
 
15.6%
3 1281
 
14.9%
1 859
 
10.0%
2 818
 
9.5%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
4 1344
 
10.4%
3 1281
 
9.9%
1 859
 
6.7%
2 818
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
4 1344
 
10.4%
3 1281
 
9.9%
1 859
 
6.7%
2 818
 
6.3%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Married
2005 
Single
1381 
Divorced
916 

Length

Max length8
Median length7
Mean length6.8919107
Min length6

Characters and Unicode

Total characters29649
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowMarried
4th rowSingle
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married 2005
46.6%
Single 1381
32.1%
Divorced 916
21.3%

Length

2023-07-15T16:10:10.379389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:10.664014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 2005
46.6%
single 1381
32.1%
divorced 916
21.3%

Most occurring characters

ValueCountFrequency (%)
r 4926
16.6%
i 4302
14.5%
e 4302
14.5%
d 2921
9.9%
M 2005
6.8%
a 2005
6.8%
S 1381
 
4.7%
n 1381
 
4.7%
g 1381
 
4.7%
l 1381
 
4.7%
Other values (4) 3664
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25347
85.5%
Uppercase Letter 4302
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4926
19.4%
i 4302
17.0%
e 4302
17.0%
d 2921
11.5%
a 2005
7.9%
n 1381
 
5.4%
g 1381
 
5.4%
l 1381
 
5.4%
v 916
 
3.6%
o 916
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
M 2005
46.6%
S 1381
32.1%
D 916
21.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 29649
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4926
16.6%
i 4302
14.5%
e 4302
14.5%
d 2921
9.9%
M 2005
6.8%
a 2005
6.8%
S 1381
 
4.7%
n 1381
 
4.7%
g 1381
 
4.7%
l 1381
 
4.7%
Other values (4) 3664
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4926
16.6%
i 4302
14.5%
e 4302
14.5%
d 2921
9.9%
M 2005
6.8%
a 2005
6.8%
S 1381
 
4.7%
n 1381
 
4.7%
g 1381
 
4.7%
l 1381
 
4.7%
Other values (4) 3664
12.4%

MonthlyIncome
Real number (ℝ)

Distinct1349
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6453.2669
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:10.936479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2096
Q12897
median4876
Q38307.75
95-th percentile17603
Maximum19999
Range18990
Interquartile range (IQR)5410.75

Descriptive statistics

Standard deviation4672.994
Coefficient of variation (CV)0.72412842
Kurtosis1.0506348
Mean6453.2669
Median Absolute Deviation (MAD)2159
Skewness1.3804188
Sum27761954
Variance21836873
MonotonicityNot monotonic
2023-07-15T16:10:11.223610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 14
 
0.3%
2380 12
 
0.3%
2741 10
 
0.2%
2404 10
 
0.2%
2610 10
 
0.2%
6347 9
 
0.2%
2042 8
 
0.2%
3452 8
 
0.2%
2109 8
 
0.2%
2973 8
 
0.2%
Other values (1339) 4205
97.7%
ValueCountFrequency (%)
1009 3
0.1%
1051 2
< 0.1%
1052 2
< 0.1%
1081 4
0.1%
1091 4
0.1%
1102 2
< 0.1%
1118 4
0.1%
1129 4
0.1%
1200 2
< 0.1%
1223 3
0.1%
ValueCountFrequency (%)
19999 2
< 0.1%
19973 3
0.1%
19943 1
 
< 0.1%
19926 2
< 0.1%
19859 2
< 0.1%
19847 1
 
< 0.1%
19845 4
0.1%
19833 2
< 0.1%
19740 4
0.1%
19717 2
< 0.1%

MonthlyRate
Real number (ℝ)

Distinct1427
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14266.381
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:11.516213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3339
Q17910.25
median14174
Q320471
95-th percentile25470
Maximum26999
Range24905
Interquartile range (IQR)12560.75

Descriptive statistics

Standard deviation7154.5843
Coefficient of variation (CV)0.50149958
Kurtosis-1.2271647
Mean14266.381
Median Absolute Deviation (MAD)6297
Skewness0.02605563
Sum61373973
Variance51188076
MonotonicityNot monotonic
2023-07-15T16:10:11.824502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 11
 
0.3%
10494 8
 
0.2%
3339 8
 
0.2%
9150 8
 
0.2%
13008 8
 
0.2%
7324 8
 
0.2%
12355 8
 
0.2%
23016 7
 
0.2%
17001 7
 
0.2%
12858 7
 
0.2%
Other values (1417) 4222
98.1%
ValueCountFrequency (%)
2094 3
0.1%
2097 2
 
< 0.1%
2104 3
0.1%
2112 4
0.1%
2122 4
0.1%
2125 5
0.1%
2137 2
 
< 0.1%
2227 4
0.1%
2243 2
 
< 0.1%
2253 2
 
< 0.1%
ValueCountFrequency (%)
26999 3
0.1%
26997 2
< 0.1%
26968 2
< 0.1%
26959 4
0.1%
26956 2
< 0.1%
26933 2
< 0.1%
26914 4
0.1%
26897 4
0.1%
26894 3
0.1%
26862 2
< 0.1%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6773594
Minimum0
Maximum9
Zeros569
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:12.078204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4906323
Coefficient of variation (CV)0.930257
Kurtosis0.044678362
Mean2.6773594
Median Absolute Deviation (MAD)1
Skewness1.044252
Sum11518
Variance6.2032492
MonotonicityNot monotonic
2023-07-15T16:10:12.283911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1545
35.9%
0 569
 
13.2%
3 477
 
11.1%
2 432
 
10.0%
4 385
 
8.9%
7 224
 
5.2%
6 201
 
4.7%
5 180
 
4.2%
9 152
 
3.5%
8 137
 
3.2%
ValueCountFrequency (%)
0 569
 
13.2%
1 1545
35.9%
2 432
 
10.0%
3 477
 
11.1%
4 385
 
8.9%
5 180
 
4.2%
6 201
 
4.7%
7 224
 
5.2%
8 137
 
3.2%
9 152
 
3.5%
ValueCountFrequency (%)
9 152
 
3.5%
8 137
 
3.2%
7 224
 
5.2%
6 201
 
4.7%
5 180
 
4.2%
4 385
 
8.9%
3 477
 
11.1%
2 432
 
10.0%
1 1545
35.9%
0 569
 
13.2%

Over18
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
True
4302 
ValueCountFrequency (%)
True 4302
100.0%
2023-07-15T16:10:12.532146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
False
3089 
True
1213 
ValueCountFrequency (%)
False 3089
71.8%
True 1213
 
28.2%
2023-07-15T16:10:12.789502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.16411
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:12.988694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6241119
Coefficient of variation (CV)0.23899272
Kurtosis-0.24455928
Mean15.16411
Median Absolute Deviation (MAD)2
Skewness0.83539105
Sum65236
Variance13.134187
MonotonicityNot monotonic
2023-07-15T16:10:13.234032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 629
14.6%
13 611
14.2%
14 597
13.9%
12 566
13.2%
15 304
7.1%
18 254
5.9%
17 250
 
5.8%
16 232
 
5.4%
19 229
 
5.3%
22 157
 
3.6%
Other values (5) 473
11.0%
ValueCountFrequency (%)
11 629
14.6%
12 566
13.2%
13 611
14.2%
14 597
13.9%
15 304
7.1%
16 232
 
5.4%
17 250
 
5.8%
18 254
5.9%
19 229
 
5.3%
20 147
 
3.4%
ValueCountFrequency (%)
25 52
 
1.2%
24 53
 
1.2%
23 86
 
2.0%
22 157
3.6%
21 135
3.1%
20 147
3.4%
19 229
5.3%
18 254
5.9%
17 250
5.8%
16 232
5.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
3.0
3672 
4.0
630 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 3672
85.4%
4.0 630
 
14.6%

Length

2023-07-15T16:10:13.480892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:13.721677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3672
85.4%
4.0 630
 
14.6%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 3672
28.5%
4 630
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
3 3672
42.7%
4 630
 
7.3%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 3672
28.5%
4 630
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 3672
28.5%
4 630
 
4.9%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
3.0
1331 
4.0
1290 
2.0
892 
1.0
789 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 1331
30.9%
4.0 1290
30.0%
2.0 892
20.7%
1.0 789
18.3%

Length

2023-07-15T16:10:13.933716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:14.211657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1331
30.9%
4.0 1290
30.0%
2.0 892
20.7%
1.0 789
18.3%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1331
 
10.3%
4 1290
 
10.0%
2 892
 
6.9%
1 789
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
3 1331
 
15.5%
4 1290
 
15.0%
2 892
 
10.4%
1 789
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1331
 
10.3%
4 1290
 
10.0%
2 892
 
6.9%
1 789
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 1331
 
10.3%
4 1290
 
10.0%
2 892
 
6.9%
1 789
 
6.1%

StandardHours
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
80.0
4302 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters17208
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80.0
2nd row80.0
3rd row80.0
4th row80.0
5th row80.0

Common Values

ValueCountFrequency (%)
80.0 4302
100.0%

Length

2023-07-15T16:10:14.441878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:14.670967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
80.0 4302
100.0%

Most occurring characters

ValueCountFrequency (%)
0 8604
50.0%
8 4302
25.0%
. 4302
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12906
75.0%
Other Punctuation 4302
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8604
66.7%
8 4302
33.3%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17208
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8604
50.0%
8 4302
25.0%
. 4302
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8604
50.0%
8 4302
25.0%
. 4302
25.0%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
0.0
1861 
1.0
1736 
2.0
462 
3.0
243 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1861
43.3%
1.0 1736
40.4%
2.0 462
 
10.7%
3.0 243
 
5.6%

Length

2023-07-15T16:10:14.878976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:15.140089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1861
43.3%
1.0 1736
40.4%
2.0 462
 
10.7%
3.0 243
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 6163
47.8%
. 4302
33.3%
1 1736
 
13.5%
2 462
 
3.6%
3 243
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6163
71.6%
1 1736
 
20.2%
2 462
 
5.4%
3 243
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6163
47.8%
. 4302
33.3%
1 1736
 
13.5%
2 462
 
3.6%
3 243
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6163
47.8%
. 4302
33.3%
1 1736
 
13.5%
2 462
 
3.6%
3 243
 
1.9%

TotalWorkingYears
Real number (ℝ)

Distinct172
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.00465
Minimum0
Maximum9939
Zeros29
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:15.403822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q317
95-th percentile2450
Maximum9939
Range9939
Interquartile range (IQR)11

Descriptive statistics

Standard deviation1508.0169
Coefficient of variation (CV)4.2598787
Kurtosis21.786282
Mean354.00465
Median Absolute Deviation (MAD)5
Skewness4.6986694
Sum1522928
Variance2274114.8
MonotonicityNot monotonic
2023-07-15T16:10:15.702106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 554
 
12.9%
6 355
 
8.3%
8 281
 
6.5%
9 272
 
6.3%
5 253
 
5.9%
1 236
 
5.5%
7 202
 
4.7%
4 166
 
3.9%
12 128
 
3.0%
3 115
 
2.7%
Other values (162) 1740
40.4%
ValueCountFrequency (%)
0 29
 
0.7%
1 236
5.5%
2 95
 
2.2%
3 115
 
2.7%
4 166
3.9%
5 253
5.9%
6 355
8.3%
7 202
4.7%
8 281
6.5%
9 272
6.3%
ValueCountFrequency (%)
9939 1
 
< 0.1%
9905 3
0.1%
9815 3
0.1%
9794 2
< 0.1%
9781 3
0.1%
9714 2
< 0.1%
9670 2
< 0.1%
9626 3
0.1%
9606 3
0.1%
9536 1
 
< 0.1%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8033473
Minimum0
Maximum6
Zeros161
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:15.964429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3025855
Coefficient of variation (CV)0.46465365
Kurtosis0.45737164
Mean2.8033473
Median Absolute Deviation (MAD)1
Skewness0.55760527
Sum12060
Variance1.6967291
MonotonicityNot monotonic
2023-07-15T16:10:16.164320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1597
37.1%
3 1429
33.2%
5 353
 
8.2%
4 349
 
8.1%
1 212
 
4.9%
6 201
 
4.7%
0 161
 
3.7%
ValueCountFrequency (%)
0 161
 
3.7%
1 212
 
4.9%
2 1597
37.1%
3 1429
33.2%
4 349
 
8.1%
5 353
 
8.2%
6 201
 
4.7%
ValueCountFrequency (%)
6 201
 
4.7%
5 353
 
8.2%
4 349
 
8.1%
3 1429
33.2%
2 1597
37.1%
1 212
 
4.9%
0 161
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
3.0
2631 
2.0
976 
4.0
453 
1.0
 
242

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12906
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 2631
61.2%
2.0 976
 
22.7%
4.0 453
 
10.5%
1.0 242
 
5.6%

Length

2023-07-15T16:10:16.404645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T16:10:16.669321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 2631
61.2%
2.0 976
 
22.7%
4.0 453
 
10.5%
1.0 242
 
5.6%

Most occurring characters

ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 2631
20.4%
2 976
 
7.6%
4 453
 
3.5%
1 242
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8604
66.7%
Other Punctuation 4302
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
50.0%
3 2631
30.6%
2 976
 
11.3%
4 453
 
5.3%
1 242
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 4302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 2631
20.4%
2 976
 
7.6%
4 453
 
3.5%
1 242
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4302
33.3%
0 4302
33.3%
3 2631
20.4%
2 976
 
7.6%
4 453
 
3.5%
1 242
 
1.9%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct166
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.85472
Minimum0
Maximum9984
Zeros126
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:16.938768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile2203
Maximum9984
Range9984
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1373.9995
Coefficient of variation (CV)4.3918133
Kurtosis25.074835
Mean312.85472
Median Absolute Deviation (MAD)3
Skewness4.967669
Sum1345901
Variance1887874.7
MonotonicityNot monotonic
2023-07-15T16:10:17.240023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 549
12.8%
1 484
11.3%
3 351
 
8.2%
2 342
 
7.9%
10 326
 
7.6%
4 307
 
7.1%
7 239
 
5.6%
8 217
 
5.0%
9 216
 
5.0%
6 209
 
4.9%
Other values (156) 1062
24.7%
ValueCountFrequency (%)
0 126
 
2.9%
1 484
11.3%
2 342
7.9%
3 351
8.2%
4 307
7.1%
5 549
12.8%
6 209
 
4.9%
7 239
5.6%
8 217
 
5.0%
9 216
 
5.0%
ValueCountFrequency (%)
9984 1
 
< 0.1%
9767 3
0.1%
9681 2
< 0.1%
9676 1
 
< 0.1%
9577 3
0.1%
9508 2
< 0.1%
9505 2
< 0.1%
9481 1
 
< 0.1%
9445 3
0.1%
9352 2
< 0.1%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct164
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean367.10948
Minimum0
Maximum9937
Zeros646
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:17.614721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile3364
Maximum9937
Range9937
Interquartile range (IQR)5

Descriptive statistics

Standard deviation1480.107
Coefficient of variation (CV)4.0317864
Kurtosis19.583032
Mean367.10948
Median Absolute Deviation (MAD)3
Skewness4.4321223
Sum1579305
Variance2190716.8
MonotonicityNot monotonic
2023-07-15T16:10:18.116573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1033
24.0%
0 646
15.0%
7 578
13.4%
3 377
 
8.8%
4 291
 
6.8%
8 259
 
6.0%
9 193
 
4.5%
1 164
 
3.8%
6 100
 
2.3%
5 82
 
1.9%
Other values (154) 579
13.5%
ValueCountFrequency (%)
0 646
15.0%
1 164
 
3.8%
2 1033
24.0%
3 377
 
8.8%
4 291
 
6.8%
5 82
 
1.9%
6 100
 
2.3%
7 578
13.4%
8 259
 
6.0%
9 193
 
4.5%
ValueCountFrequency (%)
9937 1
 
< 0.1%
9822 1
 
< 0.1%
9596 3
0.1%
9551 5
0.1%
9459 3
0.1%
9454 2
 
< 0.1%
9420 3
0.1%
9395 1
 
< 0.1%
9217 1
 
< 0.1%
9193 2
 
< 0.1%

YearsSinceLastPromotion
Real number (ℝ)

Distinct153
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.00093
Minimum0
Maximum9990
Zeros1580
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:18.636187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile3977.1
Maximum9990
Range9990
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1597.9397
Coefficient of variation (CV)4.004852
Kurtosis17.320639
Mean399.00093
Median Absolute Deviation (MAD)1
Skewness4.2330032
Sum1716502
Variance2553411.2
MonotonicityNot monotonic
2023-07-15T16:10:19.106275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1580
36.7%
1 979
22.8%
2 445
 
10.3%
7 196
 
4.6%
4 167
 
3.9%
3 150
 
3.5%
5 125
 
2.9%
6 87
 
2.0%
11 63
 
1.5%
9 44
 
1.0%
Other values (143) 466
 
10.8%
ValueCountFrequency (%)
0 1580
36.7%
1 979
22.8%
2 445
 
10.3%
3 150
 
3.5%
4 167
 
3.9%
5 125
 
2.9%
6 87
 
2.0%
7 196
 
4.6%
8 38
 
0.9%
9 44
 
1.0%
ValueCountFrequency (%)
9990 1
 
< 0.1%
9952 3
0.1%
9885 3
0.1%
9624 1
 
< 0.1%
9595 3
0.1%
9572 1
 
< 0.1%
9509 2
< 0.1%
9420 3
0.1%
9389 2
< 0.1%
9373 1
 
< 0.1%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct157
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean334.76755
Minimum0
Maximum9882
Zeros716
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2023-07-15T16:10:19.591531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile2479
Maximum9882
Range9882
Interquartile range (IQR)5

Descriptive statistics

Standard deviation1431.9919
Coefficient of variation (CV)4.2775708
Kurtosis22.290503
Mean334.76755
Median Absolute Deviation (MAD)3
Skewness4.7163004
Sum1440170
Variance2050600.8
MonotonicityNot monotonic
2023-07-15T16:10:20.049290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 955
22.2%
0 716
16.6%
7 567
13.2%
3 401
9.3%
8 284
 
6.6%
4 277
 
6.4%
1 206
 
4.8%
9 182
 
4.2%
5 84
 
2.0%
6 81
 
1.9%
Other values (147) 549
12.8%
ValueCountFrequency (%)
0 716
16.6%
1 206
 
4.8%
2 955
22.2%
3 401
9.3%
4 277
 
6.4%
5 84
 
2.0%
6 81
 
1.9%
7 567
13.2%
8 284
 
6.6%
9 182
 
4.2%
ValueCountFrequency (%)
9882 2
< 0.1%
9772 2
< 0.1%
9734 2
< 0.1%
9717 1
 
< 0.1%
9709 1
 
< 0.1%
9582 3
0.1%
9414 2
< 0.1%
9404 3
0.1%
9382 1
 
< 0.1%
9333 2
< 0.1%

Attrition
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
True
2222 
False
2080 
ValueCountFrequency (%)
True 2222
51.7%
False 2080
48.3%
2023-07-15T16:10:20.584801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-07-15T16:09:53.357963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:38.264846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:47.217656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:53.652623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:05.453119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:10.808807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:15.206326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:19.029988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:22.832094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:27.762281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:31.556658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:36.080866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:41.187144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:45.008841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:48.734629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:53.749306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:38.727245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:47.783951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:54.170428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:05.976013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:11.211947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:15.473454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:19.283373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:23.231763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:28.037266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:32.665057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:36.341809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:41.455163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:45.276948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:49.008291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:54.101875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:39.130643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:48.094421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:54.605790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:06.393311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:11.564513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:15.728888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:19.523924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:23.507455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:28.296572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:32.898407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:36.664773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:41.701001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:45.506787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:49.242737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:54.358011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:39.641664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:48.573413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:55.155797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:06.818542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:11.969156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:15.990515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:19.774092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:23.886674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:28.545087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:33.149811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:37.049716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:41.953760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:45.744035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:49.483170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:54.612829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:40.079217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:48.961911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:56.132508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:07.269438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:12.341685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:16.244515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:20.055888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:24.267535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:28.792226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:33.407233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:37.448478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:42.210927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:45.995958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:49.717917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:54.865359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:40.548820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:49.304574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:57.172887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:07.727903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:12.695499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:16.511301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:20.307631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:24.653809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:29.050265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:33.658665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:37.856008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:42.486735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:46.244808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:49.983780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:55.119943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:41.285092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:49.687692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:57.997118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:07.985330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:12.965905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:16.778888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:20.563484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:25.046765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:29.312329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:33.922630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:38.273571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:42.759798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:46.516205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:50.240518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:55.358801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:42.258303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:49.976987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:01.056501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:08.223791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:13.207883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:17.032046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:20.789473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:25.453626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:29.559144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:34.152027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:38.643198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:42.993659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:46.756628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:50.563524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:55.588428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:43.123168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:50.363457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:01.452339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:08.456334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:13.455386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:17.275329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:21.040460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:25.864122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:29.804052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:34.385187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:39.045039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:43.228302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:47.005301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:50.893607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:55.831105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:44.083039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:50.808852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:02.357952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:08.730793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:13.714518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:17.526876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:21.287975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:26.278134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:30.049201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:34.640940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:39.452973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:43.517926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:47.256681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:51.248672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:56.062318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:44.796694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:51.317337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:03.002927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:08.997446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:13.953265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:17.775310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:21.534488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:26.538485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:30.305831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:34.881626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:39.853750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:43.774352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:47.502973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:51.633153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:56.331758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:45.322764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:51.834750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:03.577701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:09.339503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:14.223005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:18.031632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:21.797811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:26.783270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:30.559128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:35.133415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:40.188728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:44.041629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:47.747519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:51.997542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:56.579605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:45.865562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:52.365411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:04.039450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:09.692540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:14.470169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:18.275164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:22.047451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:27.014282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:30.804117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:35.364207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:40.435044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:44.284971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:47.993638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:52.290910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:56.833559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:46.278458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:52.810228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:04.528805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:10.070687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:14.713192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:18.528311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:22.290292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:27.262265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:31.045763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:35.602503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:40.689318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:44.532562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:48.234099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:52.614786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:57.072444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:46.808893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:08:53.233564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:05.006715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:10.426647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:14.962134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:18.772396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:22.528322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:27.514418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:31.315886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:35.840404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:40.943489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:44.768214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:48.488666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T16:09:52.997277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-15T16:10:20.983058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EmployeeNumberAgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikeTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerBusinessTravelDepartmentEducationEducationFieldEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusOverTimePerformanceRatingRelationshipSatisfactionStockOptionLevelWorkLifeBalanceAttrition
EmployeeNumber1.0000.0680.0090.0870.023-0.0160.0060.0070.0130.0310.0150.0420.0530.0570.0480.0100.0290.0000.0180.0400.0000.0190.0350.0280.0450.0320.0000.0270.0330.0260.0150.473
Age0.0681.0000.022-0.0290.0220.425-0.0010.3170.0280.491-0.0040.1830.1480.1050.1270.0690.0620.0610.0580.0540.0410.0270.0660.0630.0660.0810.0880.0640.0580.0620.0510.088
DailyRate0.0090.0221.000-0.0190.0420.028-0.0350.0430.0300.025-0.0100.0040.0070.009-0.0020.0840.0620.0730.0740.0550.0740.0680.0640.0620.0600.1100.0300.0550.0680.0790.0620.034
DistanceFromHome0.087-0.029-0.0191.000-0.016-0.0220.026-0.021-0.009-0.018-0.036-0.001-0.010-0.0160.0080.0670.0880.0530.0670.0490.0610.0670.0450.0640.0550.0030.0520.0690.0630.0560.0420.073
HourlyRate0.0230.0220.042-0.0161.000-0.031-0.016-0.005-0.008-0.0090.012-0.047-0.050-0.062-0.0280.0580.0730.0710.0740.0720.0410.0690.0460.0790.0680.0600.0970.0700.0640.0860.0590.000
MonthlyIncome-0.0160.4250.028-0.022-0.0311.0000.0610.189-0.0350.629-0.0500.4290.3320.2260.3290.0820.1950.1110.1060.0520.0830.0800.8660.4240.0550.0990.0790.0570.0820.0890.0390.000
MonthlyRate0.006-0.001-0.0350.026-0.0160.0611.0000.024-0.0080.017-0.034-0.013-0.013-0.027-0.0300.0310.0300.0760.0610.0670.0680.0630.0690.0440.0870.0490.0520.0650.0950.0510.0820.000
NumCompaniesWorked0.0070.3170.043-0.021-0.0050.1890.0241.0000.0030.281-0.043-0.131-0.120-0.044-0.1240.0530.0810.1260.0990.0700.0250.0540.1250.1020.0610.0770.0290.0670.0750.0610.0820.032
PercentSalaryHike0.0130.0280.030-0.009-0.008-0.035-0.0080.0031.000-0.027-0.005-0.055-0.019-0.065-0.0040.0680.0620.0730.0490.0480.0830.0750.0570.0660.0480.0510.0240.9990.0710.0540.0420.000
TotalWorkingYears0.0310.4910.025-0.018-0.0090.6290.0170.281-0.0271.000-0.0080.4650.3650.2220.3730.0260.0210.0560.0470.0520.0000.0610.0450.0430.0650.0600.0000.0400.0330.0610.0650.064
TrainingTimesLastYear0.015-0.004-0.010-0.0360.012-0.050-0.034-0.043-0.005-0.0081.000-0.012-0.018-0.004-0.0090.0410.0670.0580.0690.0520.0220.0590.0530.0600.0630.0450.1050.0290.0240.0550.0000.000
YearsAtCompany0.0420.1830.004-0.001-0.0470.429-0.013-0.131-0.0550.465-0.0121.0000.6080.3950.6500.0630.0660.0570.0570.0510.0000.0280.0580.0570.0230.0270.0510.0090.0420.0570.0730.073
YearsInCurrentRole0.0530.1480.007-0.010-0.0500.332-0.013-0.120-0.0190.365-0.0180.6081.0000.3320.5130.0380.0730.0490.0360.0480.0400.0720.0550.0590.0640.0680.0490.0800.0460.0560.0470.093
YearsSinceLastPromotion0.0570.1050.009-0.016-0.0620.226-0.027-0.044-0.0650.222-0.0040.3950.3321.0000.3300.0430.0540.0520.0510.0520.0410.0900.0300.0550.0710.0370.0510.0380.0740.0470.0710.084
YearsWithCurrManager0.0480.127-0.0020.008-0.0280.329-0.030-0.124-0.0040.373-0.0090.6500.5130.3301.0000.0840.0380.0500.0410.0610.0480.0630.0410.0290.0280.0200.0470.0970.0440.0490.0490.104
BusinessTravel0.0100.0690.0840.0670.0580.0820.0310.0530.0680.0260.0410.0630.0380.0430.0841.0000.0000.0460.0200.0370.0240.0390.0420.0630.0420.0450.0360.0190.0220.0140.0220.000
Department0.0290.0620.0620.0880.0730.1950.0300.0810.0620.0210.0670.0660.0730.0540.0380.0001.0000.0000.5960.0440.0230.0440.2140.9390.0430.0380.0000.0100.0480.0380.0500.012
Education0.0000.0610.0730.0530.0710.1110.0760.1260.0730.0560.0580.0570.0490.0520.0500.0460.0001.0000.0700.0550.0410.0500.0960.0830.0510.0420.0470.0470.0460.0560.0180.009
EducationField0.0180.0580.0740.0670.0740.1060.0610.0990.0490.0470.0690.0570.0360.0510.0410.0200.5960.0701.0000.0570.0210.0530.1110.3400.0620.0420.0280.0190.0650.0600.0610.000
EnvironmentSatisfaction0.0400.0540.0550.0490.0720.0520.0670.0700.0480.0520.0520.0510.0480.0520.0610.0370.0440.0550.0571.0000.0210.0470.0410.0570.0190.0450.0620.0230.0000.0290.0330.000
Gender0.0000.0410.0740.0610.0410.0830.0680.0250.0830.0000.0220.0000.0400.0410.0480.0240.0230.0410.0210.0211.0000.0050.0580.0960.0190.0430.0280.0030.0400.0000.0040.000
JobInvolvement0.0190.0270.0680.0670.0690.0800.0630.0540.0750.0610.0590.0280.0720.0900.0630.0390.0440.0500.0530.0470.0051.0000.0530.0420.0350.0410.0000.0090.0270.0310.0290.044
JobLevel0.0350.0660.0640.0450.0460.8660.0690.1250.0570.0450.0530.0580.0550.0300.0410.0420.2140.0960.1110.0410.0580.0531.0000.5690.0300.0630.0210.0240.0460.0850.0290.043
JobRole0.0280.0630.0620.0640.0790.4240.0440.1020.0660.0430.0600.0570.0590.0550.0290.0630.9390.0830.3400.0570.0960.0420.5691.0000.0410.0860.0430.0540.0710.0720.0610.033
JobSatisfaction0.0450.0660.0600.0550.0680.0550.0870.0610.0480.0650.0630.0230.0640.0710.0280.0420.0430.0510.0620.0190.0190.0350.0300.0411.0000.0180.0500.0480.0090.0200.0320.025
MaritalStatus0.0320.0810.1100.0030.0600.0990.0490.0770.0510.0600.0450.0270.0680.0370.0200.0450.0380.0420.0420.0450.0430.0410.0630.0860.0181.0000.0170.0000.0530.5790.0270.024
OverTime0.0000.0880.0300.0520.0970.0790.0520.0290.0240.0000.1050.0510.0490.0510.0470.0360.0000.0470.0280.0620.0280.0000.0210.0430.0500.0171.0000.0000.0530.0490.0270.071
PerformanceRating0.0270.0640.0550.0690.0700.0570.0650.0670.9990.0400.0290.0090.0800.0380.0970.0190.0100.0470.0190.0230.0030.0090.0240.0540.0480.0000.0001.0000.0000.0330.0210.000
RelationshipSatisfaction0.0330.0580.0680.0630.0640.0820.0950.0750.0710.0330.0240.0420.0460.0740.0440.0220.0480.0460.0650.0000.0400.0270.0460.0710.0090.0530.0530.0001.0000.0490.0330.000
StockOptionLevel0.0260.0620.0790.0560.0860.0890.0510.0610.0540.0610.0550.0570.0560.0470.0490.0140.0380.0560.0600.0290.0000.0310.0850.0720.0200.5790.0490.0330.0491.0000.0440.033
WorkLifeBalance0.0150.0510.0620.0420.0590.0390.0820.0820.0420.0650.0000.0730.0470.0710.0490.0220.0500.0180.0610.0330.0040.0290.0290.0610.0320.0270.0270.0210.0330.0441.0000.029
Attrition0.4730.0880.0340.0730.0000.0000.0000.0320.0000.0640.0000.0730.0930.0840.1040.0000.0120.0090.0000.0000.0000.0440.0430.0330.0250.0240.0710.0000.0000.0330.0291.000

Missing values

2023-07-15T16:09:57.518057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-15T16:09:58.496412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EmployeeNumberAgeBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttrition
010430.0Travel_Rarely852.0Research & Development1.01.0Life Sciences1.04.0Male55.02.02.0Laboratory Technician4.0Married5126.015998.01.0YYes12.03.03.080.02.010.01.02.010.08.03.00.0No
1163838.0Travel_Rarely397.0Research & Development2.02.0Medical1.04.0Female54.02.03.0Manufacturing Director3.0Married7756.014199.03.0YYes19.03.04.080.01.010.06.04.05.04.00.02.0No
216426.0Travel_Rarely841.0Research & Development6.03.0Other1.03.0Female46.02.01.0Research Scientist2.0Married2368.023300.01.0YNo19.03.03.080.00.05.03.02.05.04.04.03.0No
339528.0Travel_Rarely1117.0Research & Development8.02.0Life Sciences1.04.0Female66.03.01.0Research Scientist4.0Single3310.04488.01.0YNo21.04.04.080.00.05.03.03.05.03.00.02.0No
45335.0Travel_Rarely464.0Research & Development4.02.0Other1.03.0Male75.03.01.0Laboratory Technician4.0Divorced1951.010910.01.0YNo12.03.03.080.01.01.03.03.01.00.00.00.0No
5146734.0Travel_Rarely1107.0Human Resources9.04.0Technical Degree1.01.0Female52.03.01.0Human Resources3.0Married2742.03072.01.0YNo15.03.04.080.00.02.00.03.02.02.02.02.0Yes
672732.0Travel_Rarely1018.0Research & Development3.02.0Life Sciences1.03.0Female39.03.03.0Research Director4.0Single11159.019373.03.0YNo15.03.04.080.00.010.06.03.07.07.07.07.0No
735142.0Travel_Rarely269.0Research & Development2.03.0Medical1.04.0Female56.02.01.0Laboratory Technician1.0Divorced2593.08007.00.0YYes11.03.03.080.01.010.04.03.09.06.07.08.0No
855534.0Travel_Frequently296.0Sales6.02.0Marketing1.04.0Female33.01.01.0Sales Representative3.0Divorced2351.012253.00.0YNo16.03.04.080.01.03.03.02.02.02.01.00.0Yes
925340.0Travel_Rarely989.0Research & Development4.01.0Medical1.04.0Female46.03.05.0Manager3.0Married19033.06499.01.0YNo14.03.02.080.01.021.02.03.020.08.09.09.0No
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4439797041.0Travel_Rarely582.0Research & Development28.04.0Life Sciences1.01.0Female60.02.04.0Manufacturing Director2.0Married13570.05640.00.0YNo23.04.03.080.01.021.03.03.020.03797.00.010.0Yes
4440797142.0Travel_Rarely1396.0Research & Development6.03.0Medical1.03.0Male83.03.03.0Research Director1.0Married13348.014842.09.0YNo13.03.02.080.01.01220.03.04.013.07.05.07.0Yes
4441797242.0Travel_Rarely1396.0Research & Development6.03.0Medical1.03.0Male83.03.03.0Research Director1.0Married13348.014842.09.0YNo13.03.02.080.01.01220.03.04.013.07.05.07.0Yes
4442797342.0Travel_Rarely1396.0Research & Development6.03.0Medical1.03.0Male83.03.03.0Research Director1.0Married13348.014842.09.0YNo13.03.02.080.01.01220.03.04.013.07.05.07.0No
444379748823.0Travel_Rarely621.0Research & Development15.03.0Medical1.01.0Female73.03.03.0Healthcare Representative4.0Married7978.014075.01.0YNo11.03.04.080.01.010.02.03.010.07.00.05.0Yes
444479758823.0Travel_Rarely621.0Research & Development15.03.0Medical1.01.0Female73.03.03.0Healthcare Representative4.0Married7978.014075.01.0YNo11.03.04.080.01.010.02.03.010.07.00.05.0Yes
444579768823.0Travel_Rarely621.0Research & Development15.03.0Medical1.01.0Female73.03.03.0Healthcare Representative4.0Married7978.014075.01.0YNo11.03.04.080.01.010.02.03.010.07.00.05.0Yes
4446797744.0Non-Travel381.0Research & Development918785.03.0Medical1.01.0Male49.01.01.0Laboratory Technician3.0Single3708.02104.02.0YNo14.03.03.080.00.09.05.03.05.02.01.04.0Yes
4447797844.0Non-Travel381.0Research & Development918785.03.0Medical1.01.0Male49.01.01.0Laboratory Technician3.0Single3708.02104.02.0YNo14.03.03.080.00.09.05.03.05.02.01.04.0Yes
4448797944.0Non-Travel381.0Research & Development918785.03.0Medical1.01.0Male49.01.01.0Laboratory Technician3.0Single3708.02104.02.0YNo14.03.03.080.00.09.05.03.05.02.01.04.0Yes